FD-SDG: Frequency Dropout Based Single Source Domain Generalization Framework for Retinal Vessel Segmentation

Boyang Li, Haojin Li, Yule Zhang, Heng Li, Jiangyu Chen, Fuhai Pan, Jianwen Chen, Jiang Liu

Abstract: Single-source domain generalization aims to enhance model performance on unseen target domain test sets using only a single source domain dataset, typically by mitigating domain shifts between domains. In retinal vessel segmentation tasks, differences in dataset composition, such as variations in the proportions of different diseases and imaging noise levels, are considered significant sources of domain shift. However, few previous studies have delved into the mechanisms through which this type of domain shift influences model performance. In this study, we hypothesize that disparities in dataset composition could manifest as differences in distribution patterns of frequency domain features, rendering the model susceptible to overfitting specific patterns. Building on this hypothesis, we propose a novel Frequency Dropout based Single Source Domain Generalization (FD-SDG) framework that employs a Frequency Dropout Randomization mechanism to disentangle complex co-adaptive relationships among features from different frequency bands, thereby enhancing the model’s robustness to variable frequency domain noise patterns in the sample space. Additionally, we introduce a Salient Structure Representation Normalization mechanism to align post-perturbation data features in the feature space using invariant anatomical structures. Through comparison experiments and ablation studies conducted on multiple sets of fundus images across-domain experiments, our method achieves state-of-the-art performance, underscoring its high generalizability and robustness.